An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion

摘要

The fast-developing Image fusion technique has become a necessary one in every field. Analyzing the efficiency of various fusion technologies analytically and objectively are spotted as an essentially required processes. Further, Image fusion becomes an inseparable technique in the medical field, since the role of medical images in diagnosing and identifying diseases becomes a crucial task for the radiologists and doctors at its early stage. Different modalities used in clinical applications offer unique information, unlike any other in any form. To diagnose diseases with high accuracy, clinicians require data from more than one modality. Multimodal image fusion has received wide popularity in the medical field since it enhances the accuracy of the clinical diagnosis thereby fusing the complementary information present in more than one image. Obtaining optimal value along with a reduction in cost and time in multimodal medical image fusions are a critical one. Here, in this paper a new multi-modality algorithm for medical image fusion based on the Adolescent Identity Search Algorithm (AISA) for the Non-Subsampled Shearlet Transform is proposed to obtain image optimization and to reduce the computational cost and time. The NSST is a multi-directional and multi-dimensional example of a multiscale and multi-directional wavelet transform. The input source image is decomposed into the NSST subbands at the initial stage. The boundary measure is modulated by the Adolescent Identity Search Algorithm (AISA) that fuses the sub-band in the NSST thereby reducing the complexity and increasing the computational speed. The proposed method is tested under different real-time disease datasets such as Glioma, mild Alzheimer's, and Encephalopathy with hypertension that includes similar pairs of images and analyzed different evaluation measures such as Entropy, standard deviation, structural similarity index measure,Mutual information, Average gradient, Xydeas and Petrovic metric, Peak-signal to-noise-ratio, processing time. The experimental findings and discussions indicate that the proposed algorithm outperforms other approaches and offers high quality fused images for an accurate diagnosis.